TY - GEN
T1 - Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus
AU - Gómez-Pulido, Juan A.
AU - Cortés-Toro, Enrique
AU - Durán-Domínguez, Arturo
AU - Crawford, Broderick
AU - Soto, Ricardo
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - The prediction of the students’ performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. This collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, considering matrix factorization and stochastic gradient descent. If we consider a fixed number of latent factors, the prediction error is mainly influenced by two parameters: learning rate and regularization factor. The best settings for these parameters is an optimization problem that can be tackled by soft computing techniques. In this work, we analyze three solving methods to select the optimal values of both parameters: a simple direct search, a classic evolutionary algorithm, and a novel metaheuristic. The results show the advantages of using metaheuristics instead of direct search in accuracy and computing effort terms.
AB - The prediction of the students’ performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. This collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, considering matrix factorization and stochastic gradient descent. If we consider a fixed number of latent factors, the prediction error is mainly influenced by two parameters: learning rate and regularization factor. The best settings for these parameters is an optimization problem that can be tackled by soft computing techniques. In this work, we analyze three solving methods to select the optimal values of both parameters: a simple direct search, a classic evolutionary algorithm, and a novel metaheuristic. The results show the advantages of using metaheuristics instead of direct search in accuracy and computing effort terms.
KW - Genetic algorithms
KW - Learning rate
KW - Matrix factorization
KW - Metaheuristics
KW - Prediction
KW - Recommender systems
KW - Regularization factor
KW - Stochastic gradient descent
KW - Vapour-liquid equilibrium
UR - http://www.scopus.com/inward/record.url?scp=85057093636&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03493-1_14
DO - 10.1007/978-3-030-03493-1_14
M3 - Conference contribution
AN - SCOPUS:85057093636
SN - 9783030034924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 125
EP - 133
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
A2 - Yin, Hujun
A2 - Novais, Paulo
A2 - Camacho, David
A2 - Tallón-Ballesteros, Antonio J.
PB - Springer Verlag
T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Y2 - 21 November 2018 through 23 November 2018
ER -